In digital speech and image transmission systems, the complex nature of signals to be transmitted requires high bit rates and time consuming processing. As is well known in the art, it is usually sufficient to transmit an approximation of a speech or image signal that is perceptually acceptable. Consequently, the transmission arrangements may be simplified by determining a set of indexed codes covering the range of expected signals and transmitting the indexed code closest to the signal. The process is known as vector quantization wherein vectors representing speech or image signals from a given vector space are mapped into a reduced set of vectors within the original vector space or some other representative vector space by well known clustering techniques. The reduced set of vectors, along with the associated mapping, is chosen to minimize error according to some distortion measure. This representative set of vectors is referred to as a codebook and is stored in fixed memory.
In transmission systems, the codebooks generated by vector quantization are stored at both the transmitter and the receiver. An input signal to be transmitted is processed at the transmitter by searching the stored codes for the one that best matches the signal. The index of the best matching code is transmitted as representative of the input signal. A code corresponding to the transmitted index is retrieved from the codebook at the receiver so that the transmission bit rate is greatly reduced.
The best matching code, however, only approximates the input signal. A codebook with only a few entries permits a rapid search. The selected code, however, may be a poor representation of the input signal so that it is difficult to obtain accurate signal representation. If a codebook contains sufficient entries to accurately represent all possible input signals, a time consuming search through a very large set of codes is needed to determine the closest matching code. The processing delay may exceed the time allotted for transmission of the signal. In some cases, vector quantization cannot meet the signal quality standards. In other cases, a compromise must be made between the accuracy of signal representation and the speed of transmission. Various improvements in search processing have been proposed to obtain the advantages of vector quantization with a large codebook.
U.S. Pat. No. 4,727,354 issued Feb. 23, 1988 to R. A. Lindsay discloses a system for selecting a best fit vector code in vector quantization encoding in which a sequential search through a codebook memory puts out a series of prestored associated error code vectors. These error code vectors are compared in sequence over a period of time in order to select the minimum error code vector (best fit). A clocking-sequencing arrangement enables an output latch to hold the index number which represents the particular error code vector presently having the minimum distortion. Each new set of input vector components will be sequenced to search for the minimum error code vector and index for that particular set of input vector components.
U.S. Pat. No. 4,797,925 issued Jan. 10, 1989 to Daniel Lin discloses a method for coding speech at low bit rates in which each code sequence is related to a previous code sequence so that the computational complexity of using a stored codebook is reduced. The article "Efficient Procedures for Finding the Optimum Innovation in Stochastic Coders" by I. M. Trancoso and B. S. Atal appearing in the Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1986, at pages 2375-2378, discloses an arrangement in which the signal and vectors are transformed into the frequency domain to simplify the search processing.
The article "Effect of Ordering the Codebook on the Efficiency of Partial Distance Search Algorithm for Vector Quantization" by K. K. Paliwal and V. Ramasubramanian appearing in the IEEE Transactions on Communications, Vol. 37, No. 3, May 1989, at pages 538-540, describes a search algorithm in which the distance between a codebook vector and a signal is evaluated as it is being calculated to remove vectors from consideration as early as possible. The algorithm is further improved by ordering the vectors in the codebook according to the sizes of their corresponding clusters.
The aforementioned schemes require complex signal processing for searching through complete codebooks to obtain accurate matching. It is an object of the invention to provide improved vector codebook searching with reduced signal processing requirements.